Benchmarking reports

from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	13.0	57.710617
daal4py_KNeighborsClassifier	0.0	3.0	41.437401
KNeighborsClassifier_kd_tree	0.0	6.0	58.695523
daal4py_KNeighborsClassifier_kd_tree	0.0	1.0	52.008240
KMeans_tall	0.0	1.0	42.583256
daal4py_KMeans_tall	0.0	1.0	20.514885
KMeans_short	0.0	0.0	20.296545
daal4py_KMeans_short	0.0	0.0	9.687651
LogisticRegression	0.0	1.0	5.602987
daal4py_LogisticRegression	0.0	0.0	58.531436
Ridge	0.0	0.0	0.972541
daal4py_Ridge	0.0	0.0	0.660734
total	0.0	32.0	8.778553

KNeighborsClassifier vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier	fit	0.144	0.003	1000000	1000000	100	brute	-1	1	NaN	NaN	0.484	0.001	0.297	0.001	See
1	KNeighborsClassifier	predict	0.168	0.019	1000000	1	100	brute	-1	1	1.0	0.0	0.090	0.004	1.860	0.015	See
2	KNeighborsClassifier	predict	26.566	1.048	1000000	1000	100	brute	-1	1	1.0	0.0	2.195	0.038	12.106	0.002	See
3	KNeighborsClassifier	fit	0.141	0.003	1000000	1000000	100	brute	-1	5	NaN	NaN	0.503	0.005	0.280	0.001	See
4	KNeighborsClassifier	predict	0.180	0.018	1000000	1	100	brute	-1	5	1.0	1.0	0.090	0.002	1.999	0.011	See
5	KNeighborsClassifier	predict	36.212	0.000	1000000	1000	100	brute	-1	5	1.0	1.0	2.247	0.040	16.115	0.000	See
6	KNeighborsClassifier	fit	0.141	0.003	1000000	1000000	100	brute	-1	100	NaN	NaN	0.480	0.001	0.294	0.000	See
7	KNeighborsClassifier	predict	0.184	0.018	1000000	1	100	brute	-1	100	1.0	1.0	0.087	0.001	2.121	0.010	See
8	KNeighborsClassifier	predict	36.107	0.000	1000000	1000	100	brute	-1	100	1.0	1.0	2.265	0.049	15.939	0.000	See
9	KNeighborsClassifier	fit	0.138	0.003	1000000	1000000	100	brute	1	1	NaN	NaN	0.496	0.005	0.278	0.000	See
10	KNeighborsClassifier	predict	0.185	0.003	1000000	1	100	brute	1	1	1.0	0.0	0.092	0.003	2.006	0.001	See
11	KNeighborsClassifier	predict	13.635	0.026	1000000	1000	100	brute	1	1	1.0	0.0	2.211	0.051	6.168	0.001	See
12	KNeighborsClassifier	fit	0.139	0.003	1000000	1000000	100	brute	1	5	NaN	NaN	0.484	0.001	0.288	0.000	See
13	KNeighborsClassifier	predict	0.206	0.007	1000000	1	100	brute	1	5	1.0	1.0	0.088	0.002	2.336	0.002	See
14	KNeighborsClassifier	predict	25.124	0.102	1000000	1000	100	brute	1	5	1.0	1.0	2.234	0.049	11.248	0.000	See
15	KNeighborsClassifier	fit	0.140	0.001	1000000	1000000	100	brute	1	100	NaN	NaN	0.500	0.005	0.279	0.000	See
16	KNeighborsClassifier	predict	0.198	0.002	1000000	1	100	brute	1	100	1.0	1.0	0.095	0.007	2.082	0.005	See
17	KNeighborsClassifier	predict	25.242	0.031	1000000	1000	100	brute	1	100	1.0	1.0	2.306	0.019	10.945	0.000	See
18	KNeighborsClassifier	fit	0.062	0.000	1000000	1000000	2	brute	-1	1	NaN	NaN	0.111	0.000	0.563	0.000	See
19	KNeighborsClassifier	predict	0.023	0.002	1000000	1	2	brute	-1	1	1.0	1.0	0.008	0.002	2.941	0.060	See
20	KNeighborsClassifier	predict	22.120	0.044	1000000	1000	2	brute	-1	1	1.0	1.0	0.341	0.008	64.857	0.001	See
21	KNeighborsClassifier	fit	0.062	0.000	1000000	1000000	2	brute	-1	5	NaN	NaN	0.111	0.001	0.564	0.000	See
22	KNeighborsClassifier	predict	0.032	0.001	1000000	1	2	brute	-1	5	1.0	1.0	0.007	0.001	4.432	0.012	See
23	KNeighborsClassifier	predict	32.003	0.000	1000000	1000	2	brute	-1	5	1.0	1.0	0.340	0.004	94.148	0.000	See
24	KNeighborsClassifier	fit	0.063	0.001	1000000	1000000	2	brute	-1	100	NaN	NaN	0.110	0.001	0.573	0.000	See
25	KNeighborsClassifier	predict	0.033	0.003	1000000	1	2	brute	-1	100	1.0	1.0	0.007	0.001	4.845	0.013	See
26	KNeighborsClassifier	predict	31.832	0.000	1000000	1000	2	brute	-1	100	1.0	1.0	0.401	0.006	79.376	0.000	See
27	KNeighborsClassifier	fit	0.062	0.001	1000000	1000000	2	brute	1	1	NaN	NaN	0.111	0.000	0.557	0.000	See
28	KNeighborsClassifier	predict	0.016	0.000	1000000	1	2	brute	1	1	1.0	1.0	0.006	0.000	2.493	0.002	See
29	KNeighborsClassifier	predict	10.507	0.014	1000000	1000	2	brute	1	1	1.0	1.0	0.350	0.009	30.010	0.001	See
30	KNeighborsClassifier	fit	0.062	0.000	1000000	1000000	2	brute	1	5	NaN	NaN	0.111	0.001	0.563	0.000	See
31	KNeighborsClassifier	predict	0.026	0.000	1000000	1	2	brute	1	5	1.0	1.0	0.007	0.000	3.754	0.005	See
32	KNeighborsClassifier	predict	20.683	0.048	1000000	1000	2	brute	1	5	1.0	1.0	0.343	0.008	60.310	0.001	See
33	KNeighborsClassifier	fit	0.062	0.000	1000000	1000000	2	brute	1	100	NaN	NaN	0.112	0.003	0.552	0.001	See
34	KNeighborsClassifier	predict	0.026	0.000	1000000	1	2	brute	1	100	1.0	1.0	0.007	0.000	3.804	0.001	See
35	KNeighborsClassifier	predict	20.635	0.025	1000000	1000	2	brute	1	100	1.0	1.0	0.404	0.012	51.026	0.001	See

Plots

Shared hyperparameters:

	value
algorithm	brute

KNeighborsClassifier_kd_tree vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier_kd_tree	fit	3.339	0.025	1000000	1000000	10	kd_tree	-1	1	NaN	NaN	0.813	0.012	4.110	0.000	See
1	KNeighborsClassifier_kd_tree	predict	0.003	0.000	1000000	1	10	kd_tree	-1	1	1.0	1.0	0.000	0.000	7.387	0.257	See
2	KNeighborsClassifier_kd_tree	predict	0.501	0.005	1000000	1000	10	kd_tree	-1	1	1.0	1.0	0.116	0.003	4.328	0.001	See
3	KNeighborsClassifier_kd_tree	fit	3.357	0.033	1000000	1000000	10	kd_tree	-1	5	NaN	NaN	0.827	0.020	4.059	0.001	See
4	KNeighborsClassifier_kd_tree	predict	0.003	0.000	1000000	1	10	kd_tree	-1	5	1.0	1.0	0.000	0.000	6.885	0.174	See
5	KNeighborsClassifier_kd_tree	predict	0.923	0.004	1000000	1000	10	kd_tree	-1	5	1.0	1.0	0.215	0.007	4.295	0.001	See
6	KNeighborsClassifier_kd_tree	fit	3.319	0.027	1000000	1000000	10	kd_tree	-1	100	NaN	NaN	0.796	0.019	4.169	0.001	See
7	KNeighborsClassifier_kd_tree	predict	0.006	0.002	1000000	1	10	kd_tree	-1	100	1.0	1.0	0.001	0.000	7.462	0.229	See
8	KNeighborsClassifier_kd_tree	predict	2.959	0.017	1000000	1000	10	kd_tree	-1	100	1.0	1.0	0.612	0.009	4.836	0.000	See
9	KNeighborsClassifier_kd_tree	fit	3.360	0.070	1000000	1000000	10	kd_tree	1	1	NaN	NaN	0.788	0.008	4.266	0.001	See
10	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	10	kd_tree	1	1	1.0	1.0	0.000	0.000	2.644	0.196	See
11	KNeighborsClassifier_kd_tree	predict	0.819	0.004	1000000	1000	10	kd_tree	1	1	1.0	1.0	0.117	0.006	7.006	0.003	See
12	KNeighborsClassifier_kd_tree	fit	3.340	0.044	1000000	1000000	10	kd_tree	1	5	NaN	NaN	0.829	0.025	4.032	0.001	See
13	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	10	kd_tree	1	5	1.0	1.0	0.000	0.000	2.547	0.160	See
14	KNeighborsClassifier_kd_tree	predict	1.533	0.011	1000000	1000	10	kd_tree	1	5	1.0	1.0	0.211	0.011	7.271	0.003	See
15	KNeighborsClassifier_kd_tree	fit	3.302	0.043	1000000	1000000	10	kd_tree	1	100	NaN	NaN	0.773	0.028	4.273	0.002	See
16	KNeighborsClassifier_kd_tree	predict	0.002	0.001	1000000	1	10	kd_tree	1	100	1.0	1.0	0.001	0.000	2.428	0.217	See
17	KNeighborsClassifier_kd_tree	predict	5.000	0.054	1000000	1000	10	kd_tree	1	100	1.0	1.0	0.617	0.009	8.104	0.000	See
18	KNeighborsClassifier_kd_tree	fit	1.389	0.005	1000000	1000000	2	kd_tree	-1	1	NaN	NaN	0.538	0.019	2.583	0.001	See
19	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	2	kd_tree	-1	1	1.0	1.0	0.000	0.000	26.121	0.645	See
20	KNeighborsClassifier_kd_tree	predict	0.029	0.002	1000000	1000	2	kd_tree	-1	1	1.0	1.0	0.001	0.000	25.426	0.148	See
21	KNeighborsClassifier_kd_tree	fit	1.397	0.016	1000000	1000000	2	kd_tree	-1	5	NaN	NaN	0.531	0.017	2.633	0.001	See
22	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	2	kd_tree	-1	5	1.0	1.0	0.000	0.000	20.695	0.513	See
23	KNeighborsClassifier_kd_tree	predict	0.033	0.004	1000000	1000	2	kd_tree	-1	5	1.0	1.0	0.001	0.000	28.643	0.086	See
24	KNeighborsClassifier_kd_tree	fit	1.402	0.021	1000000	1000000	2	kd_tree	-1	100	NaN	NaN	0.529	0.014	2.651	0.001	See
25	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	2	kd_tree	-1	100	1.0	1.0	0.000	0.000	23.246	0.675	See
26	KNeighborsClassifier_kd_tree	predict	0.053	0.004	1000000	1000	2	kd_tree	-1	100	1.0	1.0	0.008	0.001	6.950	0.013	See
27	KNeighborsClassifier_kd_tree	fit	1.393	0.007	1000000	1000000	2	kd_tree	1	1	NaN	NaN	0.534	0.011	2.607	0.000	See
28	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	1	1.0	1.0	0.000	0.000	5.773	0.584	See
29	KNeighborsClassifier_kd_tree	predict	0.026	0.000	1000000	1000	2	kd_tree	1	1	1.0	1.0	0.001	0.000	24.105	0.174	See
30	KNeighborsClassifier_kd_tree	fit	1.405	0.012	1000000	1000000	2	kd_tree	1	5	NaN	NaN	0.535	0.004	2.626	0.000	See
31	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	5	1.0	1.0	0.000	0.000	5.914	0.665	See
32	KNeighborsClassifier_kd_tree	predict	0.029	0.000	1000000	1000	2	kd_tree	1	5	1.0	1.0	0.001	0.000	19.324	0.072	See
33	KNeighborsClassifier_kd_tree	fit	1.415	0.019	1000000	1000000	2	kd_tree	1	100	NaN	NaN	0.556	0.019	2.543	0.001	See
34	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	100	1.0	1.0	0.000	0.000	5.630	0.599	See
35	KNeighborsClassifier_kd_tree	predict	0.059	0.001	1000000	1000	2	kd_tree	1	100	1.0	1.0	0.009	0.002	6.431	0.054	See

Plots

Shared hyperparameters:

	value
algorithm	kd_tree

KMeans_tall vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_tall	fit	0.683	0.022	1000000	1000000	2	full	k-means++	30	3	1	0.0	30.0	NaN	30.0	NaN	0.528	0.043	1.295	0.008	See
1	KMeans_tall	predict	0.000	0.000	1000000	1	2	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.291	0.622	See
2	KMeans_tall	predict	0.000	0.000	1000000	1000	2	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.977	0.465	See
3	KMeans_tall	fit	0.578	0.011	1000000	1000000	2	full	random	30	3	1	0.0	30.0	NaN	30.0	NaN	0.476	0.035	1.214	0.006	See
4	KMeans_tall	predict	0.000	0.000	1000000	1	2	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.897	0.490	See
5	KMeans_tall	predict	0.000	0.000	1000000	1000	2	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.527	0.442	See
6	KMeans_tall	fit	6.903	0.126	1000000	1000000	100	full	k-means++	30	3	1	0.0	30.0	NaN	30.0	NaN	3.181	0.038	2.170	0.000	See
7	KMeans_tall	predict	0.000	0.000	1000000	1	100	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.651	0.426	See
8	KMeans_tall	predict	0.001	0.000	1000000	1000	100	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.170	0.223	See
9	KMeans_tall	fit	6.211	0.044	1000000	1000000	100	full	random	30	3	1	0.0	30.0	NaN	30.0	NaN	2.984	0.067	2.081	0.001	See
10	KMeans_tall	predict	0.000	0.000	1000000	1	100	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.596	0.609	See
11	KMeans_tall	predict	0.001	0.000	1000000	1000	100	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.994	0.223	See

Plots

Shared hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

KMeans_short vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_short	fit	0.364	0.018	10000	10000	2	full	k-means++	30	300	1	0.0	22.0	NaN	29.0	NaN	0.115	0.003	3.157	0.003	See
1	KMeans_short	predict	0.000	0.000	10000	1	2	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.102	0.550	See
2	KMeans_short	predict	0.001	0.001	10000	1000	2	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	1.302	0.288	See
3	KMeans_short	fit	0.142	0.004	10000	10000	2	full	random	30	300	1	0.0	30.0	NaN	30.0	NaN	0.053	0.005	2.681	0.009	See
4	KMeans_short	predict	0.000	0.000	10000	1	2	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.760	0.586	See
5	KMeans_short	predict	0.001	0.000	10000	1000	2	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	1.058	0.067	See
6	KMeans_short	fit	0.877	0.028	10000	10000	100	full	k-means++	30	300	1	0.0	20.0	NaN	18.0	NaN	0.434	0.024	2.018	0.004	See
7	KMeans_short	predict	0.000	0.000	10000	1	100	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.894	0.275	See
8	KMeans_short	predict	0.007	0.003	10000	1000	100	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	4.701	0.249	See
9	KMeans_short	fit	0.247	0.031	10000	10000	100	full	random	30	300	1	0.0	23.0	NaN	24.0	NaN	0.206	0.022	1.200	0.027	See
10	KMeans_short	predict	0.000	0.000	10000	1	100	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.947	0.271	See
11	KMeans_short	predict	0.007	0.003	10000	1000	100	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	4.992	0.273	See

Plots

Shared hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	30
n_init	1
tol	0.0

LogisticRegression vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	n_iter	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	LogisticRegression	fit	11.974	0.113	1000000	1000000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	12.049	0.068	0.994	0.000	See
1	LogisticRegression	predict	0.000	0.000	1000000	1	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.000	0.000	0.589	1.390	See
2	LogisticRegression	predict	0.000	0.000	1000000	1000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.000	0.000	1.191	0.182	See
3	LogisticRegression	fit	0.810	0.017	1000	1000	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[26]	0.863	0.022	0.939	0.001	See
4	LogisticRegression	predict	0.000	0.000	1000	1	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.002	0.002	0.081	1.663	See
5	LogisticRegression	predict	0.002	0.000	1000	100	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.003	0.000	0.562	0.038	See

Plots

Shared hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

Ridge vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	n_iter	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	Ridge	fit	0.039	0.001	1000	1000	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.023	0.003	1.687	0.015	See
1	Ridge	predict	0.000	0.000	1000	1	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.642	1.130	See
2	Ridge	predict	0.000	0.000	1000	100	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.711	0.356	See
3	Ridge	fit	0.008	0.000	10000	10000	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.004	0.002	2.181	0.206	See
4	Ridge	predict	0.000	0.000	10000	1	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.643	1.062	See
5	Ridge	predict	0.000	0.000	10000	1000	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.724	0.402	See

Plots

Shared hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	False
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

Environment information

{
  "system_info": {
    "python": "3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:22:27)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1046-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "0.24.1",
    "numpy": "1.20.2",
    "scipy": "1.6.2",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": null,
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.12.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.12",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}